class_docstr: Apply Gaussian Blur to the PIL image. Take the radius and probability of
  application as the parameter.
  
  This transform was used in SimCLR - https://arxiv.org/abs/2002.05709
docstr: Instantiates ImgPilGaussianBlur from configuration.
  
  Args:
      config (Dict): arguments for for the transform
  
  Returns:
      ImgPilGaussianBlur instance.
docstr_tok: ["Instantiates","ImgPilGaussianBlur","from","configuration",".","Args",":","config","(","Dict",")",":","arguments","for","for","the","transform","Returns",":","ImgPilGaussianBlur","instance","."]
code: def from_config(cls, config: Dict[str, Any]) -> "ImgPilGaussianBlur":
      """
      Instantiates ImgPilGaussianBlur from configuration.
  
      Args:
          config (Dict): arguments for for the transform
  
      Returns:
          ImgPilGaussianBlur instance.
      """
      prob = config.get("p", 0.5)
      radius_min = config.get("radius_min", 0.1)
      radius_max = config.get("radius_max", 2.0)
      return cls(p=prob, radius_min=radius_min, radius_max=radius_max)
  
file_path: vissl/vissl/data/ssl_transforms/img_pil_gaussian_blur.py
imports: import random
  from typing import Any, Dict
  import numpy
  from classy_vision.dataset.transforms import register_transform
  from classy_vision.dataset.transforms.classy_transform import ClassyTransform
  from PIL import ImageFilter
label_desc: Use image node_id 3 for calling the ImgPilGaussianBlur obj's underlying member method code with example usage: obj.from_config(cls, config) and returns: cls